{"type":"ScholarlyArticle","title":"Model-Based Synthesis of Incremental and Correct Estimators for Discrete Event Systems","authors":[{"name":"Stéphanie Roussel","slug":"stephanie-roussel","given":"Stéphanie","family":"Roussel","affiliation":"ONERA / DTIS , Université de Toulouse , F-31055 Toulouse - France"},{"name":"Xavier Pucel","slug":"xavier-pucel","given":"Xavier","family":"Pucel","affiliation":"ONERA / DTIS , Université de Toulouse , F-31055 Toulouse - France"},{"name":"Valentin Bouziat","slug":"valentin-bouziat","given":"Valentin","family":"Bouziat","affiliation":"ONERA / DTIS , Université de Toulouse , F-31055 Toulouse - France"},{"name":"Louise Travé-Massuyès","slug":"louise-trave-massuyes","given":"Louise","family":"Travé-Massuyès","affiliation":"LAAS-CNRS , Université de Toulouse , CNRS , Toulouse , France"}],"abstract":"State tracking, i.e. estimating the state over time, is always an important problem in autonomous dynamic systems. Run-time requirements advocate for incremental estimation and memory limitations lead us to consider an estimation strategy that retains only one state out of the set of candidate estimates at each time step. This avoids the ambiguity of a high number of candidate estimates and allows the decision system to be fed with a clear input.\r\nHowever, this strategy may lead to dead-ends in the continuation of the execution. In this paper, we show that single-state trackability can be expressed in terms of the simulation relation between automata. This allows us to provide a complexity bound and a way to build estimators endowed with this property and, moreover, customizable along some correctness criteria. Our implementation relies on the Sat Modulo Theory solver MonoSAT and experiments show that our encoding scales up and applies to real world scenarios.","doi":"10.24963/ijcai.2020/261","url":"https://ijcai.org/proceedings/2020/261","pdf":"https://ijcai.org/proceedings/2020/0261.pdf","markdown":"https://ijcai.org/proceedings/2020/261.md","bibtex":"https://ijcai.org/proceedings/2020/bibtex/261","volume":{"slug":"2020","title":"Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2020","url":"https://ijcai.org/proceedings/2020/","year":2020,"location":"Yokohama","dates":"January 2021 (postponed from 11-17 July 2020)","editors":"Christian Bessiere","isbn":"978-0-9992411-6-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2020"},"section":"Knowledge Representation and Reasoning","track":"Main track","number":261,"pages":{"first":1884,"last":1890},"keywords":["Constraints and SAT: Satisfiability Modulo Theories","Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning"],"videos":[{"label":"Video","url":"https://videos.ijcai20.org/24685/file_24685.m3u8","length_sec":286,"provider":"hls"}],"references":[{"position":1,"raw":"[Bayless et al., 2015] Sam Bayless, Noah Bayless, Hol- ger H. 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